Understanding and Predicting Delay in Reciprocal Relations

Reciprocity in directed networks points to user's willingness to return favors in building mutual interactions. High reciprocity has been widely observed in many directed social media networks such as following relations in Twitter and Tumblr. Therefore, reciprocal relations between users are often regarded as a basic mechanism to create stable social ties and play a crucial role in the formation and evolution of networks. Each reciprocity relation is formed by two parasocial links in a back-and-forth manner with a time delay. Hence, understanding the delay can help us gain better insights into the underlying mechanisms of network dynamics. Meanwhile, the accurate prediction of delay has practical implications in advancing a variety of real-world applications such as friend recommendation and marketing campaign. For example, by knowing when will users follow back, service providers can focus on the users with a potential long reciprocal delay for effective targeted marketing. This paper presents the initial investigation of the time delay in reciprocal relations. Our study is based on a large-scale directed network from Tumblr that consists of 62.8 million users and 3.1 billion user following relations with a timespan of multiple years (from 31 Oct 2007 to 24 Jul 2013). We reveal a number of interesting patterns about the delay that motivate the development of a principled learning model to predict the delay in reciprocal relations. Experimental results on the above mentioned dynamic networks corroborate the effectiveness of the proposed delay prediction model.Comment: 10 page

Attributed Network Embedding for Learning in a Dynamic Environment

Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation for each node in the network. The learned embeddings could advance various learning tasks such as node classification, network clustering, and link prediction. Most, if not all, of the existing works, are overwhelmingly performed in the context of plain and static networks. Nonetheless, in reality, network structure often evolves over time with addition/deletion of links and nodes. Also, a vast majority of real-world networks are associated with a rich set of node attributes, and their attribute values are also naturally changing, with the emerging of new content patterns and the fading of old content patterns. These changing characteristics motivate us to seek an effective embedding representation to capture network and attribute evolving patterns, which is of fundamental importance for learning in a dynamic environment. To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly. In this paper, we tackle this problem by proposing a novel dynamic attributed network embedding framework - DANE. In particular, DANE first provides an offline method for a consensus embedding and then leverages matrix perturbation theory to maintain the freshness of the end embedding results in an online manner. We perform extensive experiments on both synthetic and real attributed networks to corroborate the effectiveness and efficiency of the proposed framework.Comment: 10 page

Circadian Clock Gene Expression and Drug/Toxicant Interactions as Novel Targets of Chronopharmacology and Chronotoxicology

Circadian rhythms are driven and maintained by circadian clock gene networks in both brain and peripheral organs. In the liver, circadian rhythms produce oscillation in drug Phase-I, Phase-II, and Phase-III (transporters) metabolism genes, which in turn would affect drug disposition and detoxication, resulting in diurnal variations of efficacy and toxicity when drugs are given at different times of the day. On the other hand, drugs and toxicants could affect circadian clock gene expression to produce biological effects leading to therapeutic or toxic outcomes. This chapter reviewed the relevant literature and a dozen of publications from our work, discussed the interactions of circadian clock genes with drugs and/or toxicants to better understand the importance of circadian clock gene expression as novel targets in Pharmacology and Toxicology

On-Policy Pixel-Level Grasping Across the Gap Between Simulation and Reality

Grasp detection in cluttered scenes is a very challenging task for robots. Generating synthetic grasping data is a popular way to train and test grasp methods, as is Dex-net and GraspNet; yet, these methods generate training grasps on 3D synthetic object models, but evaluate at images or point clouds with different distributions, which reduces performance on real scenes due to sparse grasp labels and covariate shift. To solve existing problems, we propose a novel on-policy grasp detection method, which can train and test on the same distribution with dense pixel-level grasp labels generated on RGB-D images. A Parallel-Depth Grasp Generation (PDG-Generation) method is proposed to generate a parallel depth image through a new imaging model of projecting points in parallel; then this method generates multiple candidate grasps for each pixel and obtains robust grasps through flatness detection, force-closure metric and collision detection. Then, a large comprehensive Pixel-Level Grasp Pose Dataset (PLGP-Dataset) is constructed and released; distinguished with previous datasets with off-policy data and sparse grasp samples, this dataset is the first pixel-level grasp dataset, with the on-policy distribution where grasps are generated based on depth images. Lastly, we build and test a series of pixel-level grasp detection networks with a data augmentation process for imbalance training, which learn grasp poses in a decoupled manner on the input RGB-D images. Extensive experiments show that our on-policy grasp method can largely overcome the gap between simulation and reality, and achieves the state-of-the-art performance. Code and data are provided at https://github.com/liuchunsense/PLGP-Dataset

Frame Flexible Network

Existing video recognition algorithms always conduct different training pipelines for inputs with different frame numbers, which requires repetitive training operations and multiplying storage costs. If we evaluate the model using other frames which are not used in training, we observe the performance will drop significantly (see Fig.1), which is summarized as Temporal Frequency Deviation phenomenon. To fix this issue, we propose a general framework, named Frame Flexible Network (FFN), which not only enables the model to be evaluated at different frames to adjust its computation, but also reduces the memory costs of storing multiple models significantly. Concretely, FFN integrates several sets of training sequences, involves Multi-Frequency Alignment (MFAL) to learn temporal frequency invariant representations, and leverages Multi-Frequency Adaptation (MFAD) to further strengthen the representation abilities. Comprehensive empirical validations using various architectures and popular benchmarks solidly demonstrate the effectiveness and generalization of FFN (e.g., 7.08/5.15/2.17% performance gain at Frame 4/8/16 on Something-Something V1 dataset over Uniformer). Code is available at https://github.com/BeSpontaneous/FFN.Comment: Accepted by CVPR202

Proteomic analysis of swine serum following highly virulent classical swine fever virus infection

<p>Abstract</p> <p>Background</p> <p>Classical swine fever virus (CSFV) belongs to the genus <it>Pestivirus </it>within the family <it>Flaviviridae</it>. Virulent strains of classical swine fever virus (CSFV) cause severe disease in pigs characterized by immunosuppression, thrombocytopenia and disseminated intravascular coagulation, which causes significant economic losses to the pig industry worldwide.</p> <p>Methods</p> <p>To reveal proteomic changes in swine serum during the acute stage of lethal CSFV infection, 5 of 10 pigs were inoculated with the virulent CSFV Shimen strain, the remainder serving as uninfected controls. A serum sample was taken at 3 days post-infection from each swine, at a stage when there were no clinical symptoms other than increased rectal temperatures (≥40°C). The samples were treated to remove serum albumin and immunoglobulin (IgG), and then subjected to two-dimension differential gel electrophoresis.</p> <p>Results</p> <p>Quantitative intensity analysis revealed 17 protein spots showing at least 1.5-fold quantitative alteration in expression. Ten spots were successfully identified by MALDI-TOF MS or LTQ MS. Expression of 4 proteins was increased and 6 decreased in CSFV-infected pigs. Functions of these proteins included blood coagulation, anti-inflammatory activity and angiogenesis.</p> <p>Conclusion</p> <p>These proteins with altered expression may have important implications in the pathogenesis of classical swine fever and provide a clue for identification of biomarkers for classical swine fever early diagnosis.</p

Interactions of Bacteria with Monolithic Lateral Silicon Nanospikes Inside a Microfluidic Channel

This paper presents a new strategy of integrating lateral silicon nanospikes using metal-assisted chemical etching (MacEtch) on the sidewall of micropillars for on-chip bacterial study. Silicon nanospikes have been reported to be able to kill bacteria without using chemicals and offer a new route to kill bacteria and can prevent the overuse of antibiotics to reduce bacteria. We demonstrated a new methodology to fabricate a chip with integrated silicon nanospikes onto the sidewalls of micropillars inside the microfluidic channel and attested its interactions with the representative gram-negative bacteria Escherichia coli. The results of colony-forming unit (CFU) calculation showed that 80% bacteria lost their viability after passing through the chip. Moreover, the results of adenosine triphosphate (ATP) measurement indicated that the chip with lateral silicon nanospikes could extract more than two times ATP contents compared with the chip without lateral silicon nanospikes, showing potential for using the chip with lateral silicon nanospikes as a bacterial lysing module

β-Nd2Mo4O15

The title compound, dineodymium(III) tetra­molybdate(VI), has been prepared by a flux technique and is the second polymorph of composition Nd2Mo4O15. The crystal structure is isotypic with those of Ce2Mo4O15 and Pr2Mo4O15. It features a three-dimensional network composed of distorted edge- and corner-sharing NdO7 polyhedra, NdO8 polyhedra, MoO4 tetra­hedra and MoO6 octa­hedra